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Active Dendrites Enable Strong but Sparse Inputs to Determine Orientation Selectivity

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Active Dendrites Enable Strong but Sparse Inputs to Determine Orientation Selectivity Active dendrites enable strong but sparse inputs to determine orientation selectivity Lea Goetza, Arnd Rotha, and Michael Häussera,1 aWolfson Institute for Biomedical Research, University College London, London WC1E 6BT, United Kingdom Edited by Tobias Bonhoeffer, Max Planck Institute of Neurobiology, Munich, Germany, and approved June 9, 2021 (received for review August 16, 2020) The dendrites of neocortical pyramidal neurons are excitable. How- trigger dendritic Na+ spikes and NMDA spikes. We also provide ever, it is unknown how synaptic inputs engage nonlinear dendritic a quantitative explanation for how these dendritic spikes determine mechanisms during sensory processing in vivo, and how they in turn neuronal output in vivo. Our results show that dendritic spikes can influence action potential output. Here, we provide a quantitative be triggered by a surprisingly small number of synaptic inputs—in account of the relationship between synaptic inputs, nonlinear den- some cases even by single synapses. We also find that during sensory dritic events, and action potential output. We developed a detailed processing, already few dendritic spikes are effective at driving pyramidal neuron model constrained by in vivo dendritic recordings. somatic output. Overall, this strategy allows a remarkably small We drive this model with realistic input patterns constrained by sensory number of strong synaptic inputs to dominate neural output, responses measured in vivo and connectivity measured in vitro. We which may reduce the number of neurons required to represent a show mechanistically that under realistic conditions, dendritic Na+ and given sensory feature. NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a Results surprisingly small number of strong synaptic inputs, in some cases An Active Dendritic Model Reproduces Responses to Visual Stimulation. even by single synapses. We predict that dendritic excitability al- To study the mechanisms of synaptic integration during visual lows the 1% strongest synaptic inputs of a neuron to control the stimulation in vivo, we built and combined models of the sensory- tuning of its output. Active dendrites therefore allow smaller sub- evoked synaptic input and the postsynaptic pyramidal neuron circuits consisting of only a few strongly connected neurons to (Fig. 1 A and B; Materials and Methods). The postsynaptic model achieve selectivity for specific sensory features. satisfies a large set of experimental constraints obtained in vitro NEUROSCIENCE (22, 23) and in vivo (21, 23, 24) (SI Appendix, Fig. S1). In par- dendrite | pyramidal cell | synaptic integration | visual cortex | ticular, we constrained dendritic excitability and membrane po- dendritic spike tential dynamics during sensory processing by taking advantage of direct patch-clamp recordings from the dendrites of L2/3 py- here is longstanding evidence from in vitro experiments that ramidal neurons in mouse V1 in vivo (17). Tdendrites of mammalian neurons are electrically excitable (1, 2), The synaptic input model (SI Appendix,Fig.S2) satisfies con- and theoretical work has demonstrated that these active properties straints set by the experimentally measured distribution of firing can be exploited for computations so that single neurons can rates of the presynaptic neuron population (17), the overall number perform functions that could otherwise only be performed by a of synaptic inputs to the neuron (25), the mean number of synaptic network (3–7). Recently, technical breakthroughs have enabled contacts per connection (2.8) (19), and the connection probability dendritic integration to be studied in vivo using both imaging (26) and strength (18) as a function of the difference in orientation and electrophysiological techniques (8, 9). These experiments preference of the pre- and postsynaptic neurons (Materials and have revealed that the integration of synaptic events in vivo can Methods). A key assumption of the synaptic input model is the be highly nonlinear and that this process influences the response properties of single neurons and neuronal populations in vivo Significance (10–16). For example, patch-clamp recordings from dendrites in mouse primary visual cortex (V1) have demonstrated that dendritic An active pyramidal cell model, constrained by physiological and spikes are triggered by visual input and that they may contribute anatomical data, was used to simulate dendritic integration in to the orientation selectivity of somatic membrane potential (17). vivo. The model shows that small numbers of strong excitatory However, important mechanistic questions are still unanswered. synapses can trigger dendritic Na+ and NMDA spikes. Moreover, How many synaptic inputs must be locally coactive on a dendrite only a few dendritic spikes are sufficient to drive a single output to recruit dendritic spikes? What is the contribution of individual action potential. As a consequence, as few as 1% of the synaptic dendritic spikes to somatic action potential (AP) output and its inputs to a neuron can determine the tuning of somatic output orientation selectivity? Moreover, we do not understand how the in vivo. These results suggest that dendritic spikes can help to answers to these questions depend on the type of dendritic spike. make sensory representations more efficient and flexible: they Finally, how do active dendrites, by supporting dendritic spikes, require fewer connections to sustain them, and only a small influence which synaptic inputs control AP output and its tuning? number of connections need to be changed to encode a different These issues are extremely challenging to address experimen- stimulus and alter the response properties of a neuron. tally. We have therefore taken a modeling approach, constrained by in vitro and in vivo experimental data, in order to provide a Author contributions: L.G., A.R., and M.H. designed research; L.G. and A.R. performed quantitative understanding of the relationship between synaptic research; L.G. and A.R. analyzed data; and L.G., A.R., and M.H. wrote the paper. input, dendritic spikes, and AP output during sensory processing The authors declare no competing interest. inV1.Weconstructedadetailedactivemodelofalayer(L)2/3 This article is a PNAS Direct Submission. pyramidal neuron in mouse V1 and combined this with a model of This open access article is distributed under Creative Commons Attribution-NonCommercial- the presynaptic inputs it receives during visual stimulation with NoDerivatives License 4.0 (CC BY-NC-ND). drifting gratings in vivo (17–21). 1To whom correspondence may be addressed. Email: [email protected]. Our model reproduces key features of the experimental data This article contains supporting information online at https://www.pnas.org/lookup/suppl/ on dendritic and somatic responses to visual stimulation as ob- doi:10.1073/pnas.2017339118/-/DCSupplemental. served in vivo and allows us to identify the synaptic inputs that Published July 23, 2021. PNAS 2021 Vol. 118 No. 30 e2017339118 https://doi.org/10.1073/pnas.2017339118 | 1of12 Downloaded by guest on October 1, 2021 ABsoma dendrite 0º 45º 90º dendrite soma 135º 100 μm 20 mV 0.2 s C Experiment Model 300 μm 300 μm 300 μm 256 μm 256 μm 243 μm 167 μm 167 μm 167 μm 212 μm 204 μm 212 μm 150 μm 150 μm 150 μm 155 μm 155 μm 185 μm Distance from soma > 100 μm > 100 μm > 100 μm 106 μm 107 μm 125 μm 100 μm 100 μm 100 μm 97 μm 99 μm 97 μm 20 mV 200 ms 75 μm 75 μm 86 μm 88 μm 88 μm Fig. 1. A pyramidal cell model with active dendrites reproduces dendritic responses to visual stimulation D in vivo. (A) Morphology of the L2/3 pyramidal neu- ) ron used for the model, with pipettes indicating the -1 locations of the recordings shown in B.(B) Example of somatic (Left) and dendritic (light blue, Right) ) -1 responses of the postsynaptic model neuron to sim- ulated synaptic inputs corresponding to visual stimuli with orientations 0°, 45°, 90°, and 135°, where 90° is the preferred orientation of the model neuron. (C) Comparison of dendritic patch-clamp recordings in vivo [pink, Left; data from Smith et al. (17)] with dendritic recordings in the model neuron (light blue, Mean event rate (s Right). Note that vertical scale bars differ between traces. (D) Comparison of event rates in the experi- ment [pink; using data from Smith et al. (17)] and Max instantaneous event rate (s Variance to mean ratio (Fano factor) Variance model (light blue). Events are defined as episodes of monotonically rising membrane potential with a soma dendrite soma dendrite soma dendrite baseline-to-peak difference of more than 10 mV. lognormal distribution of synaptic weights, which is supported events, as well as the variance-to-mean ratio (Fano factor) of the by experimental studies (27–31) reporting a small number of rel- distribution of event counts in a given time interval, were within atively strong connections among many weaker ones. Since no the same range in model and experiment (Fig. 1D). direct and complete recordings of the synaptic input received by a L2/3 neuron in vivo are available, and there is conflicting evidence Biophysical Definition of Dendritic Na+ Spikes and NMDA Spikes. regarding the spatial and temporal structure of active synapses Identifying and discriminating different types of active den- (32–37), we chose an approach in which different synaptic inputs dritic events is difficult in experimental dendritic recordings, are activated independently of each other so that any spatial or especially in vivo where it has not yet been possible to record temporal clustering occurs by chance.Wealsotestedanalternative dendritic and somatic voltages simultaneously. This means that synaptic input model in which synaptic inputs with similar tuning the signals arising from synaptic inputs, locally generated den- are spatially clustered. dritic spikes and backpropagating APs (bAPs), are difficult to The combined model reproduced somatic and dendritic re- separate. In the model, the ability to accurately track all of the sponses to visual stimulation in vivo, including their orientation relevant variables, namely synaptic currents, membrane poten- tuning (17) (Fig.
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